

'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_UpperCAmelCase  :		Optional[Any]  =		(7_20, 12_80)  # Height, Width
_UpperCAmelCase  :		str  =		(0.4, 0.6)  # if height or width lower than this scale, drop it.
_UpperCAmelCase  :		Any  =		1 / 1_00
_UpperCAmelCase  :		Tuple  =		''''''
_UpperCAmelCase  :		Union[str, Any]  =		''''''
_UpperCAmelCase  :		Any  =		''''''
_UpperCAmelCase  :		Tuple  =		2_50




def 	UpperCamelCase   (       )		->	None:




  '''simple docstring'''



  lowercase							, lowercase									=get_dataset(lowercase_     ,					lowercase_	)
  for index in range(lowercase_	):
    lowercase									=random.sample(range(len(lowercase_	)	)     ,					4	)
    lowercase							, lowercase							, lowercase									=update_image_and_anno(
        lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					lowercase_     ,					filter_scale=lowercase_     ,					)

    # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
    lowercase									=random_chars(3_2	)
    lowercase									=path.split(os.sep	)[-1].rsplit('''.'''     ,					1	)[0]
    lowercase									=f'{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}'
    cva.imwrite(f'{file_root}.jpg'     ,					lowercase_     ,					[cva.IMWRITE_JPEG_QUALITY, 8_5]	)
    print(f'Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}'	)
    lowercase									=[]
    for anno in new_annos:
      lowercase									=anno[3] - anno[1]
      lowercase									=anno[4] - anno[2]
      lowercase									=anno[1] + width / 2
      lowercase									=anno[2] + height / 2
      lowercase									=f'{anno[0]} {x_center} {y_center} {width} {height}'
      annos_list.append(lowercase_	)
    with open(f'{file_root}.txt'     ,					'''w'''	) as outfile:
      outfile.write('''\n'''.join(line for line in annos_list	)	)




def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    str	)		->	tuple[list, list]:




  '''simple docstring'''



  lowercase									=[]
  lowercase									=[]
  for label_file in glob.glob(os.path.join(lowercase_     ,					'''*.txt'''	)	):
    lowercase									=label_file.split(os.sep	)[-1].rsplit('''.'''     ,					1	)[0]
    with open(lowercase_	) as in_file:
      lowercase									=in_file.readlines()
    lowercase									=os.path.join(lowercase_     ,					f'{label_name}.jpg'	)

    lowercase									=[]
    for obj_list in obj_lists:
      lowercase									=obj_list.rstrip('''\n'''	).split(''' '''	)
      lowercase									=float(obj[1]	) - float(obj[3]	) / 2
      lowercase									=float(obj[2]	) - float(obj[4]	) / 2
      lowercase									=float(obj[1]	) + float(obj[3]	) / 2
      lowercase									=float(obj[2]	) + float(obj[4]	) / 2

      boxes.append([int(obj[0]	), xmin, ymin, xmax, ymax]	)
    if not boxes:
      continue
    img_paths.append(lowercase_	)
    labels.append(lowercase_	)
  return img_paths, labels




def 	UpperCamelCase   (       lowercase_						:    list     ,					lowercase_						:    list     ,					lowercase_						:    list[int]     ,					lowercase_						:    tuple[int, int]     ,					lowercase_						:    tuple[float, float]     ,					lowercase_						:    float = 0.0     ,					)		->	tuple[list, list, str]:




  '''simple docstring'''



  lowercase									=np.zeros([output_size[0], output_size[1], 3]     ,					dtype=np.uinta	)
  lowercase									=scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  lowercase									=scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
  lowercase									=int(scale_x * output_size[1]	)
  lowercase									=int(scale_y * output_size[0]	)

  lowercase									=[]
  lowercase									=[]
  for i, index in enumerate(lowercase_	):
    lowercase									=all_img_list[index]
    path_list.append(lowercase_	)
    lowercase									=all_annos[index]
    lowercase									=cva.imread(lowercase_	)
    if i == 0:  # top-left
      lowercase									=cva.resize(lowercase_     ,					(divid_point_x, divid_point_y)	)
      lowercase									=img
      for bbox in img_annos:
        lowercase									=bbox[1] * scale_x
        lowercase									=bbox[2] * scale_y
        lowercase									=bbox[3] * scale_x
        lowercase									=bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
    elif i == 1:  # top-right
      lowercase									=cva.resize(lowercase_     ,					(output_size[1] - divid_point_x, divid_point_y)	)
      lowercase									=img
      for bbox in img_annos:
        lowercase									=scale_x + bbox[1] * (1 - scale_x)
        lowercase									=bbox[2] * scale_y
        lowercase									=scale_x + bbox[3] * (1 - scale_x)
        lowercase									=bbox[4] * scale_y
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
    elif i == 2:  # bottom-left
      lowercase									=cva.resize(lowercase_     ,					(divid_point_x, output_size[0] - divid_point_y)	)
      lowercase									=img
      for bbox in img_annos:
        lowercase									=bbox[1] * scale_x
        lowercase									=scale_y + bbox[2] * (1 - scale_y)
        lowercase									=bbox[3] * scale_x
        lowercase									=scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)
    else:  # bottom-right
      lowercase									=cva.resize(
          lowercase_     ,					(output_size[1] - divid_point_x, output_size[0] - divid_point_y)	)
      lowercase									=img
      for bbox in img_annos:
        lowercase									=scale_x + bbox[1] * (1 - scale_x)
        lowercase									=scale_y + bbox[2] * (1 - scale_y)
        lowercase									=scale_x + bbox[3] * (1 - scale_x)
        lowercase									=scale_y + bbox[4] * (1 - scale_y)
        new_anno.append([bbox[0], xmin, ymin, xmax, ymax]	)

    # Remove bounding box small than scale of filter
  if filter_scale > 0:
    lowercase									=[
        anno
        for anno in new_anno
        if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
    ]

  return output_img, new_anno, path_list[0]




def 	UpperCamelCase   (       lowercase_						:    int	)		->	str:




  '''simple docstring'''



  assert number_char > 1, "The number of character should greater than 1"
  lowercase									=ascii_lowercase + digits
  return "".join(random.choice(lowercase_	) for _ in range(lowercase_	)	)


if __name__ == "__main__":
     main()
     print('''DONE ✅''')







"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






